期刊
CONSTRUCTION AND BUILDING MATERIALS
卷 359, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.conbuildmat.2022.129438
关键词
Masonry building; Crack segmentation; Deep learning; Measurement; Image processing
资金
- Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education [2020R1A6A1A03038540]
- National Research Foundation of Korea (NRF) grant - Korean government, Ministry of Science and ICT (MSIT) [2021R1F1A1046339]
- Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries (IPET) through Digital Breeding Transformation Technology Development Program
- Ministry of Agriculture, Food and Rural Affairs (MAFRA) [322063-03-1-SB010]
- National Research Foundation of Korea [2021R1F1A1046339] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
This research focuses on implementing computer vision techniques and deep learning to automate crack segmentation and real-life crack length measurement of masonry walls. The experimental results demonstrate that deep learning-based crack segmentation outperforms previous approaches and can provide accurate measurements.
While there have been a considerable number of studies on computer vision (CV)-based crack detection on concrete/asphalt public facilities, such as sewers and tunnels, masonry-related structures have received less attention. This research seeks to implement an automated crack segmentation and a real-life crack length measurement of masonry walls using CV techniques and deep learning. The main contributions include (1) a large dataset of manually labelled images about various types of Korea masonry walls; (2) a careful performance evaluation of various deep learning-based crack segmentation models, including U-Net, DeepLabV3+, and FPN; and (3) a novel algorithm to extract real-life crack length measurement by detecting the brick units. The experimental results showed that deep learning-based masonry crack segmentation performed significantly better than previous approaches and could provide a real-life crack measurement. Therefore, it has a huge po-tential for motivating masonry-based structure investigation.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据